Take Home Exercise 02

Analysing and Visualising the distribution of Airbnb listings in 2019 and 2021, and how Covid-19 affected the distribution.

Kwek Yi Chen https://example.com/kwekyichen
09-16-2021

1 Introduction

In recent years, the emergence of numerous peer-based business models has empowered individuals across the globe to become micro-entrepreneurs, earning money from their idle property and spare time. This phenomenon, entitled ‘the sharing economy’, has seen unprecedented growth in terms of user numbers, enabling new avenues of economic and social interaction (Sundararajan, 2016).

Since its inception in 2008, Airbnb has expanded into over 34,000 cities across 191 countries. Uncommonly for a sharing economy company, Airbnb transitioned into profitability in 2016, demonstrating proof-of-concept for the validity of sharing economy within the global market (Stone & Zaleski, 2017).

Interestingly, Singapore is one of the global city that has yet to legalise short-term rentals offered by platforms such as Airbnb (Read more at https://www.todayonline.com/singapore/short-term-home-sharing-remains-illegal-singapore-airbnb-disappointed). However, during my recent visit to Inside Airbnb (http://insideairbnb.com/about.html), an independent, non-commercial set of tools and data that allows anyone to explore how Airbnb is really being used in cities around the world, it came to my attention that there are data sets for Singapore.

This analysis consist of two sections: 1) Airbnb Distribution in 2019 2) Impact of COVID-19

2 Data

3 Setting Up the Environment

The following code chunk install the following packages:

Show code 
packages = c('maptools', 'sf', 'raster','spatstat', 'tmap', 'tidyr', 'readr')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

4 Spatial Data Wrangling

4.1 Importing Geospatial Data

4.1.1 MrtLrt

4.1.1.1 Importing

The following code uses st_read function of sf package to import MrtLrt geospatial data sets.

Show code 
mrtlrt_sf <- st_read(dsn = "data/geospatial", layer="MRTLRTStnPtt")

Reading layer `MRTLRTStnPtt' from data source 
  `C:\kwekyichen\IS415_blog\_posts\2021-09-16-takehome-ex02\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 185 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 6138.311 ymin: 27555.06 xmax: 45254.86 ymax: 47854.2
Projected CRS: SVY21

The MrtLrt data have 185 features with 3 fields. The projected CRS is SVY21. The geometry type is in point.

4.1.1.2 Check CRS

Next, we use st_crs function of sf to check the CRS.

Show code 
st_crs(mrtlrt_sf)

Coordinate Reference System:
  User input: SVY21 
  wkt:
PROJCRS["SVY21",
    BASEGEOGCRS["SVY21[WGS84]",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]],
            ID["EPSG",6326]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["Degree",0.0174532925199433]]],
    CONVERSION["unnamed",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]]]

From the result above, even though the mrtlrt_sf is projected in svy21, the last line indicates that the EPSG is 9001. It is wrong because the correct EPSG code for svy21 should be 3414.

4.1.1.3 Assign EPSG code to a simple feature data frame

The following code chunk will assign the correct EPSG code to mrtlrt_sf using st_set_crs function of sf package

Show code 
mrtlrt_sf <- st_set_crs(mrtlrt_sf, 3414)

Now that we have assign the correct EPSG code to mrtlrt_sf, we will check the CRS again using st_crs function of sf

4.1.1.4 Check CRS again

Show code 
st_crs(mrtlrt_sf)

Coordinate Reference System:
  User input: EPSG:3414 
  wkt:
PROJCRS["SVY21 / Singapore TM",
    BASEGEOGCRS["SVY21",
        DATUM["SVY21",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4757]],
    CONVERSION["Singapore Transverse Mercator",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["northing (N)",north,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["easting (E)",east,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping."],
        AREA["Singapore - onshore and offshore."],
        BBOX[1.13,103.59,1.47,104.07]],
    ID["EPSG",3414]]

From the result above, we have successfully assign EPSG 3414 to mrtlrt_sf.

4.1.2 CostalOutline

4.1.2.1 Importing

The following code uses st_read function of sf package to import CostalOutline geospatial data sets.

Show code 
sg_sf <- st_read(dsn = "data/geospatial", layer="CostalOutline")

Reading layer `CostalOutline' from data source 
  `C:\kwekyichen\IS415_blog\_posts\2021-09-16-takehome-ex02\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 60 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2663.926 ymin: 16357.98 xmax: 56047.79 ymax: 50244.03
Projected CRS: SVY21

The CostalOutline data have 60 features with 4 fields. The projected CRS is SVY21. The geometry type is polygon.

4.1.2.2 Check CRS

Show code 
st_crs(sg_sf)

Coordinate Reference System:
  User input: SVY21 
  wkt:
PROJCRS["SVY21",
    BASEGEOGCRS["SVY21[WGS84]",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]],
            ID["EPSG",6326]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["Degree",0.0174532925199433]]],
    CONVERSION["unnamed",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]]]

Similarly, even though the sg_sf is projected in svy21, the last line indicates that the EPSG is 9001. It is wrong because the correct EPSG code for svy21 should be 3414.

4.1.2.3 Assign EPSG code to a simple feature data frame

The following code chunk will assign the correct EPSG code to sg_sf using st_set_crs function of sf package

Show code 
sg_sf <- st_set_crs(sg_sf, 3414)

Now that we have assign the correct EPSG code to sg_sf, we will check the CRS again using st_crs function of sf

4.1.2.4 Check CRS again

Show code 
st_crs(sg_sf)

Coordinate Reference System:
  User input: EPSG:3414 
  wkt:
PROJCRS["SVY21 / Singapore TM",
    BASEGEOGCRS["SVY21",
        DATUM["SVY21",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4757]],
    CONVERSION["Singapore Transverse Mercator",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["northing (N)",north,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["easting (E)",east,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping."],
        AREA["Singapore - onshore and offshore."],
        BBOX[1.13,103.59,1.47,104.07]],
    ID["EPSG",3414]]

We have successfully set the EPSG to 3414 for sg_sf.

4.1.3 MPSZ

4.1.3.1 Importing

The following code uses st_read function of sf package to import MP14_SUBZONE_WEB_PL geospatial data sets.

Show code 
mpsz_sf <- st_read(dsn = "data/geospatial", layer = "MP14_SUBZONE_WEB_PL")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\kwekyichen\IS415_blog\_posts\2021-09-16-takehome-ex02\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

The mpsz data have 323 features with 15 fields. The projected CRS is SVY21. The geometry type is multipolygon.

4.1.3.2 Check CRS

Show code 
st_crs(mpsz_sf)

Coordinate Reference System:
  User input: SVY21 
  wkt:
PROJCRS["SVY21",
    BASEGEOGCRS["SVY21[WGS84]",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]],
            ID["EPSG",6326]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["Degree",0.0174532925199433]]],
    CONVERSION["unnamed",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]]]

Similarly, even though the mpsz_sf is projected in svy21, the last line indicates that the EPSG is 9001. It is wrong because the correct EPSG code for svy21 should be 3414.

4.1.3.3 Assign EPSG code to a simple feature data frame

The following code chunk will assign the correct EPSG code to mpsz_sf using st_set_crs function of sf package

Show code 
mpsz_sf <- st_set_crs(mpsz_sf, 3414)

Now that we have assign the correct EPSG code to mpsz_sf, we will check the CRS again using st_crs function of sf

4.1.3.4 Check CRS again

Show code 
st_crs(mpsz_sf)

Coordinate Reference System:
  User input: EPSG:3414 
  wkt:
PROJCRS["SVY21 / Singapore TM",
    BASEGEOGCRS["SVY21",
        DATUM["SVY21",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4757]],
    CONVERSION["Singapore Transverse Mercator",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["northing (N)",north,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["easting (E)",east,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping."],
        AREA["Singapore - onshore and offshore."],
        BBOX[1.13,103.59,1.47,104.07]],
    ID["EPSG",3414]]

We have successfully assign ESPG 3414 to mpsz_sf.

4.2 Importing Aspatial Data

4.2.1 Hotel

4.2.1.1 Importing

The following code uses read_csv function of readr package to import Hotel aspatial data sets.

Show code 
hotels <- read_csv("data/aspatial/hotels.csv")

4.2.1.2 Examine the file

The following code uses list function of base R to examine the imported file

Show code 
list(hotels)

[[1]]
# A tibble: 422 x 9
   NAME     ADDRESSPOSTALCODE ADDRESSSTREETNAME HYPERLINK   TOTALROOMS
   <chr>                <dbl> <chr>             <chr>            <dbl>
 1 Jayleen~             59390 25 New Bridge Ro~ jayleencla~         20
 2 JEN Sin~            238858 277 Orchard Road~ singaporeo~        499
 3 JEN Sin~            249716 1A Cuscaden Road  singaporet~        565
 4 JH HOTEL            399041 8 LORONG 10 GEYL~ anguskt@gm~         42
 5 Ji Hote~            238485 11 PENANG LANE I~ <NA>                81
 6 Jin Don~            399007 33 Lorong 12 Gey~ <NA>                33
 7 Joyfor ~            389226 135 Geylang Road  joyfor.sg@~         17
 8 JW Marr~            189763 30 Beach Road     jw.sinjw.c~        634
 9 K Hotel             399199 44 LORONG 6 GEYL~ <NA>                56
10 Holiday~            423371 88 EAST COAST RO~ <NA>               451
# ... with 412 more rows, and 4 more variables: KEEPERNAME <chr>,
#   Lat <dbl>, Lng <dbl>, ICON_NAME <chr>

The data contains 422 rows and 9 columns. There are ‘Lat’ and ‘Lng’ column for coordinates. They are in decimal degree format, hence we will assume the data is wgs84.

We will next check if there are any NA value in ‘Lat’ and ‘Lng’ using is.na function of base R package.

Show code 
any(is.na(hotels$Lat))

[1] FALSE
Show code 
any(is.na(hotels$Lng))

[1] FALSE

We can confirmed that there are no NA values and we can proceed to create a simple feature data frame.

4.2.1.3 Create simple feature data frame from aspatial data

The following code 1. convert hotels data into simple feature data frame using st_as_sf feature of sf package. 2. ‘Lng’ and ‘Lat’ column are set as the coords arguments. 3. CRS were set as 4326 since we assumed that the data is wgs84 4. st_transform function of sf package then helps to transform the newly created simple feature data frame into svy21 5. tm_shape and tm_dots function of tmap plots the transformed hotels_sf

Show code 
hotels_sf <- st_as_sf(hotels,
                    coords = c("Lng", 
                               "Lat"),
                    crs=4326) %>%
  st_transform(crs = 3414)

tm_shape(hotels_sf)+
  tm_dots()

4.2.2 Tourism

4.2.2.1 Importing

The following code uses read_csv function of readr package to import Tourism aspatial data sets.

Show code 
tourism <- read_csv("data/aspatial/tourism.csv")

4.2.2.2 Examine the file

The following code uses list function of base R to examine the imported file

Show code 
list(tourism)

[[1]]
# A tibble: 107 x 17
   NAME   DESCRIPTION  ADDRESSSTREETNA~ HYPERLINK  PHOTOURL  URL_PATH 
   <chr>  <chr>        <chr>            <chr>      <chr>     <chr>    
 1 China~ Experience ~ 48 Pagoda Street http://ww~ www.your~ www.your~
 2 Thian~ Beautifully~ 158 Telok Ayer ~ http://ww~ www.your~ www.your~
 3 Euras~ This engagi~ 139 Ceylon Road  http://ww~ www.your~ www.your~
 4 Shoph~ With many b~ <NA>             <NA>       www.your~ www.your~
 5 Capit~ The queen o~ 11 Stamford Road http://ww~ www.your~ www.your~
 6 Espla~ This boldly~ 1 Esplanade Dri~ http://ww~ www.your~ www.your~
 7 Garde~ The lush an~ 18 Marina Garde~ http://ww~ www.your~ www.your~
 8 The I~ There’s mor~ 180-226 Depot R~ http://ww~ <NA>      www.your~
 9 PARKR~ PARKROYAL o~ 3 Upper Pickeri~ http://ww~ www.your~ www.your~
10 Lasal~ How can som~ 1 McNally Street https://w~ www.your~ www.your~
# ... with 97 more rows, and 11 more variables: IMAGE_ALT_TEXT <chr>,
#   PHOTOCREDITS <chr>, LASTMODIFIED <dttm>, LATITUDE <dbl>,
#   LONGTITUDE <dbl>, META_DESCRIPTION <chr>, OPENING_HOURS <chr>,
#   Lat <dbl>, Lng <dbl>, ICON_NAME <chr>, ADDRESSPOSTALCODE <dbl>

The tourism data contains 107 rows and 17 columns. There are ‘LATITUDE’ and ‘LONGTITUDE’ column, as well as ‘Lat’ and ‘Lng’ coluumn for coordinates. We will be using ‘LATITUDE’ and ‘LONGTITUDE’ column since they are the same. They are in decimal degree format, hence we will assume the data is wgs84.

We will next check if there are any NA value in ‘LATITUDE’ and ‘LONGTITUDE’ using is.na function of base R package.

Show code 
any(is.na(tourism$LATITUDE))

[1] TRUE
Show code 
any(is.na(tourism$LATITUDE))

[1] TRUE

There are NA values and we will need to drop them before creating a simple feature data frame.

The following code chunk drop rows of data as long as the LATITUDE is NA.

Show code 
tourism <- tourism %>%
            drop_na("LATITUDE")

4.2.2.3 Create simple feature data frame from aspatial data

The following code 1. convert tourism data into simple feature data frame using st_as_sf feature of sf package. 2. ‘LONGTITUDE’ and ‘LATITUDE’ column are set as the coords arguments. 3. CRS were set as 4326 since we assumed that the data is wgs84 4. st_transform function of sf package then helps to transform the newly created simple feature data frame into svy21 5. tm_shape and tm_dots function of tmap plots the transformed tourism_sf

Show code 
tourism_sf <- st_as_sf(tourism,
                    coords = c("LONGTITUDE", 
                               "LATITUDE"),
                    crs=4326) %>%
  st_transform(crs = 3414)

tm_shape(tourism_sf)+
  tm_dots()

4.2.3 Airbnb0619

4.2.3.1 Importing

The following code uses read_csv function of readr package to import Airbnb listings30062019.csv aspatial data sets.

Show code 
airbnb0619 <- read_csv("data/aspatial/listings30062019.csv")

4.2.3.2 Examine the file

The following code uses list function of base R to examine the imported file

Show code 
list(airbnb0619)

[[1]]
# A tibble: 8,293 x 16
       id name        host_id host_name neighbourhood_g~ neighbourhood
    <dbl> <chr>         <dbl> <chr>     <chr>            <chr>        
 1  49091 "COZICOMFO~  266763 Francesca North Region     Woodlands    
 2  50646 "Pleasant ~  227796 Sujatha   Central Region   Bukit Timah  
 3  56334 "COZICOMFO~  266763 Francesca North Region     Woodlands    
 4  71609 "Ensuite R~  367042 Belinda   East Region      Tampines     
 5  71896 "B&B  Room~  367042 Belinda   East Region      Tampines     
 6  71903 "Room 2-ne~  367042 Belinda   East Region      Tampines     
 7  71907 "3rd level~  367042 Belinda   East Region      Tampines     
 8 151547 "En-suite ~  729617 Kanchuya  Central Region   Orchard      
 9 241503 "Long stay~ 1017645 Bianca    East Region      Bedok        
10 241508 "Long stay~ 1017645 Bianca    East Region      Bedok        
# ... with 8,283 more rows, and 10 more variables: latitude <dbl>,
#   longitude <dbl>, room_type <chr>, price <dbl>,
#   minimum_nights <dbl>, number_of_reviews <dbl>,
#   last_review <date>, reviews_per_month <dbl>,
#   calculated_host_listings_count <dbl>, availability_365 <dbl>

The airbnb0619 data contains 8293 rows and 16 columns. There are ‘latitude’ and ‘longitude’ column for coordinates. They are in decimal degree format, hence we will assume the data is wgs84.

We will next check if there are any NA value in ‘latitude’ and ‘longitude’ using is.na function of base R package.

Show code 
any(is.na(airbnb0619$latitude))

[1] FALSE
Show code 
any(is.na(airbnb0619$longitude))

[1] FALSE

Since there are no NA values, we can proceed to create simple features data frame for airbnb0619

4.2.3.3 Create simple feature data frame from aspatial data

The following code 1. convert airbnb0619 data into simple feature data frame using st_as_sf feature of sf package. 2. ‘longitude’ and ‘latitude’ column are set as the coords arguments. 3. CRS were set as 4326 since we assumed that the data is wgs84 4. st_transform function of sf package then helps to transform the newly created simple feature data frame into svy21 5. tm_shape and tm_dots function of tmap plots the transformed airbnb0619_sf

Show code 
airbnb0619_sf <- st_as_sf(airbnb0619,
                    coords = c("longitude", 
                               "latitude"),
                    crs=4326) %>%
  st_transform(crs = 3414)

tm_shape(airbnb0619_sf)+
  tm_dots()

4.2.4 Airbnb0721

4.2.4.1 Importing

The following code uses read_csv function of readr package to import Airbnb listings19072021.csv aspatial data sets.

Show code 
airbnb0721 <- read_csv("data/aspatial/listings19072021.csv")

4.2.4.2 Examine the file

The following code uses list function of base R to examine the imported file

Show code 
list(airbnb0721)

[[1]]
# A tibble: 4,252 x 16
       id name        host_id host_name neighbourhood_g~ neighbourhood
    <dbl> <chr>         <dbl> <chr>     <chr>            <chr>        
 1  50646 Pleasant R~  227796 Sujatha   Central Region   Bukit Timah  
 2  71609 Ensuite Ro~  367042 Belinda   East Region      Tampines     
 3  71896 B&B  Room ~  367042 Belinda   East Region      Tampines     
 4  71903 Room 2-nea~  367042 Belinda   East Region      Tampines     
 5 275343 Convenient~ 1439258 Joyce     Central Region   Bukit Merah  
 6 275344 15 mins to~ 1439258 Joyce     Central Region   Bukit Merah  
 7 294281 5 mins wal~ 1521514 Elizabeth Central Region   Newton       
 8 301247 Nice room ~ 1552002 Rahul     Central Region   Geylang      
 9 324945 20 Mins to~ 1439258 Joyce     Central Region   Bukit Merah  
10 330089 Accomo@ RE~ 1439258 Joyce     Central Region   Bukit Merah  
# ... with 4,242 more rows, and 10 more variables: latitude <dbl>,
#   longitude <dbl>, room_type <chr>, price <dbl>,
#   minimum_nights <dbl>, number_of_reviews <dbl>,
#   last_review <date>, reviews_per_month <dbl>,
#   calculated_host_listings_count <dbl>, availability_365 <dbl>

The airbnb0721 data contains 4252 rows and 16 columns, lesser than airbnb0619. There are ‘latitude’ and ‘longitude’ column for coordinates. They are in decimal degree format, hence we will assume the data is wgs84.

We will next check if there are any NA value in ‘latitude’ and ‘longitude’ using is.na function of base R package.

Show code 
any(is.na(airbnb0721$latitude))

[1] FALSE
Show code 
any(is.na(airbnb0721$longitude))

[1] FALSE

We can confirm that there are no latitude and longitude with NA value.

4.2.4.3 Create simple feature data frame from aspatial data

The following code 1. convert airbnb0721 data into simple feature data frame using st_as_sf feature of sf package. 2. ‘longitude’ and ‘latitude’ column are set as the coords arguments. 3. CRS were set as 4326 since we assumed that the data is wgs84 4. st_transform function of sf package then helps to transform the newly created simple feature data frame into svy21 5. tm_shape and tm_dots function of tmap plots the transformed airbnb0721_sf

Show code 
airbnb0721_sf <- st_as_sf(airbnb0721,
                    coords = c("longitude", 
                               "latitude"),
                    crs=4326) %>%
  st_transform(crs = 3414)

tm_shape(airbnb0721_sf)+
  tm_dots()

4.3 Geospatial Data wrangling

After importing geospatial and aspatial data, we will convert the simple feature data frame to sp’s Spatial* class.

4.3.1 Converting sf data frames to sp’s Spatial* class

The following code chunk converts all the geospatial data from simple feature data frame to sp’s Spatial* class using as_Spatial functions of sf package.

Show code 
airbnb0619 <- as_Spatial(airbnb0619_sf)
airbnb0721 <- as_Spatial(airbnb0721_sf)
hotels <- as_Spatial(hotels_sf)
tourism <- as_Spatial(tourism_sf)
mrtlrt <- as_Spatial(mrtlrt_sf)
sg <- as_Spatial(sg_sf)
mpsz <- as_Spatial(mpsz_sf)

Check each Spatial* class

Show code 
airbnb0619

class       : SpatialPointsDataFrame 
features    : 8293 
extent      : 7215.566, 44098.31, 25166.35, 49226.35  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 14
names       :       id,                                              name,   host_id,                host_name, neighbourhood_group, neighbourhood,       room_type, price, minimum_nights, number_of_reviews, last_review, reviews_per_month, calculated_host_listings_count, availability_365 
min values  :    49091,                                                 -,     23666, (Email hidden by Airbnb),      Central Region,    Ang Mo Kio, Entire home/apt,     0,              1,                 0,       15656,              0.01,                              1,                0 
max values  : 36053005, ZR2- NEW! Sunny & Modern Apt 4 mins to Orchard Rd, 271165196,                   Zuzana,         West Region,        Yishun,     Shared room, 13999,           1000,               308,       18072,             12.09,                            277,              365
Show code 
airbnb0721

class       : SpatialPointsDataFrame 
features    : 4252 
extent      : 7406.989, 43337.89, 25330, 48391.55  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 14
names       :       id,                                               name,   host_id,        host_name, neighbourhood_group, neighbourhood,       room_type, price, minimum_nights, number_of_reviews, last_review, reviews_per_month, calculated_host_listings_count, availability_365 
min values  :    50646, !! CozyRoom@City Center,Little India,FarrerParkMRT,     23666, <U+4F73><U+4F73>,      Central Region,    Ang Mo Kio, Entire home/apt,    13,              1,                 0,       15456,              0.01,                              1,                0 
max values  : 51122424,                 Zimmer nah der MRT (Downtown Line), 411376818,             Zuzu,         West Region,        Yishun,     Shared room, 13400,           1000,               370,       18826,                77,                            165,              365
Show code 
hotels

class       : SpatialPointsDataFrame 
features    : 422 
extent      : 5939.241, 45334.18, 25379.44, 44562.4  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 7
names       :                              NAME, ADDRESSPOSTALCODE,                                 ADDRESSSTREETNAME,         HYPERLINK, TOTALROOMS,     KEEPERNAME, ICON_NAME 
min values  :                      30 BENCOOLEN,             18956,                                 1 Bayfront Avenue, 96ytlim@gmail.com,          4,  Adel Aramouni, hotel.gif 
max values  : YotelAir Singapore Changi Airport,            819666, 99 IRRAWADDY ROAD, # 22-00 ROYAL SQUARE AT NOVENA, zubair@dam.com.sg,       2561, Zhang YuanQing, hotel.gif
Show code 
tourism

class       : SpatialPointsDataFrame 
features    : 106 
extent      : 11380.23, 43659.54, 22869.34, 47596.73  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 15
names       :                                NAME,                                                                                                                                                  DESCRIPTION,                  ADDRESSSTREETNAME,                            HYPERLINK,                                                                                                                             PHOTOURL,                                                                                          URL_PATH,                                                                                                          IMAGE_ALT_TEXT,                 PHOTOCREDITS,   LASTMODIFIED,                                                                                             META_DESCRIPTION,                                                                                                                                                                           OPENING_HOURS,              Lat,              Lng,        ICON_NAME, ADDRESSPOSTALCODE 
min values  : Adventure Cove Waterpark™ Singapore, A feat of engineering, an architectural statement and a sheer aesthetic triumph, Marina Bay Sands<sup>®</sup> has upped the ante for buildings in Singapore.,                       1 Beach Road,                   http://acm.org.sg/,                                  /content/dam/desktop/global/see-do-singapore/architecture/hajjah-fatimah-mosque-carousel01-rect.jpg, www.yoursingapore.com/en/see-do-singapore/architecture/historical/capitol-building-singapore.html,               Adults and kids of all ages who are not even science buffs will have fun at the Singapore Science Centre., ©Darren Soh/National Gallery, 1427691447.648, A tranquil patch of imperial China in the west of Singapore is pleasant respite from the bustle of the city.,                                                                                                                                                 50th storey Skybridge, Daily, 9am –10pm, 1.22309649975255, 103.683980000372, tourist_spot.gif,                 0 
max values  :          Victoria Theatre Singapore,                                                    With so many attractions packed into this 15-km stretch of beaches, you’ll never run out of things to do., Seng Poh Road and Tiong Bahru Road, https://www.pub.gov.sg/marinabarrage, www.yoursingapore.com/content/dam/desktop/global/see-do-singapore/recreation-leisure/universal-studios-singapore-carousel01-rect.jpg,      www.yoursingapore.com/en/see-do-singapore/recreation-leisure/viewpoints/singapore-flyer.html, Whether you prefer water sports, rollerblading or cycling, find a myriad of things to do at East Coast Park, Singapore.,  Wildlife Reserves Singapore, 1446544541.364,                                     With the Henderson Waves bridge, form meets function to stunning effect., Visits are by appointment only.Visitors must sign up in advance for heritage tours which fall on:Monday, 2pm – 3pm,Tuesday, 6.30pm – 7.30pm,Thursday, 10am – 11am,Saturday, 11am – 12pm, 1.44672000006097, 103.974030000359, tourist_spot.gif,                 0
Show code 
mrtlrt

class       : SpatialPointsDataFrame 
features    : 185 
extent      : 6138.311, 45254.86, 27555.06, 47854.2  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 3
names       : OBJECTID,              STN_NAME, STN_NO 
min values  :        0, ADMIRALTY MRT STATION,    BP1 
max values  :      191,    YISHUN MRT STATION,    TE3
Show code 
sg

class       : SpatialPolygonsDataFrame 
features    : 60 
extent      : 2663.926, 56047.79, 16357.98, 50244.03  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 4
names       : GDO_GID, MSLINK, MAPID,              COSTAL_NAM 
min values  :       1,      1,     0,             ISLAND LINK 
max values  :      60,     67,     0, SINGAPORE - MAIN ISLAND
Show code 
mpsz

class       : SpatialPolygonsDataFrame 
features    : 323 
extent      : 2667.538, 56396.44, 15748.72, 50256.33  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 15
names       : OBJECTID, SUBZONE_NO, SUBZONE_N, SUBZONE_C, CA_IND, PLN_AREA_N, PLN_AREA_C,       REGION_N, REGION_C,          INC_CRC, FMEL_UPD_D,     X_ADDR,     Y_ADDR,    SHAPE_Leng,    SHAPE_Area 
min values  :        1,          1, ADMIRALTY,    AMSZ01,      N, ANG MO KIO,         AM, CENTRAL REGION,       CR, 00F5E30B5C9B7AD8,      16409,  5092.8949,  19579.069, 871.554887798, 39437.9352703 
max values  :      323,         17,    YUNNAN,    YSSZ09,      Y,     YISHUN,         YS,    WEST REGION,       WR, FFCCF172717C2EAF,      16409, 50424.7923, 49552.7904, 68083.9364708,  69748298.792

We can see that all the geospatial data are converted to their respective Spatial* Classes.

4.3.2 Converting the Spatial* class into generic sp format

Next, we will convert Spatial* classes into Spatial object so that we can convert it into ppp object.

The following code chunk converts Spatial* classes into generic sp format using as function which sets the classes to their respective sp format.

Show code 
airbnb0619_sp <- as(airbnb0619, "SpatialPoints")
airbnb0721_sp <- as(airbnb0721, "SpatialPoints")
hotels_sp <- as(hotels, "SpatialPoints")
tourism_sp <- as(tourism, "SpatialPoints")
mrtlrt_sp <- as(mrtlrt, "SpatialPoints")
sg_sp <- as(sg, "SpatialPolygons")
mpsz_sp <- as(mpsz, "SpatialPolygons")

4.3.3 Converting the generic sp format into spatstat’s ppp format

We will next convert the spatial data into spatstat’s ppp format.

The following code chunk converts spatial data into spatstat’s ppp format using as function.

Show code 
airbnb0619_ppp <- as(airbnb0619_sp, "ppp")
airbnb0721_ppp <- as(airbnb0721_sp, "ppp")
hotels_ppp <- as(hotels_sp, "ppp")
tourism_ppp <- as(tourism_sp, "ppp")
mrtlrt_ppp <- as(mrtlrt_sp, "ppp")

4.3.4 Handling duplicates in ppp object

4.3.4.1 Check for dupliciates duplicates in each ppp object

The following code chunk use any and duplicated function of base R package to check for duplicates, and sum up the count of duplicated points using sum function of base R package and multiplicity function of spatstat package.

Show code 
any(duplicated(airbnb0619_ppp))

[1] TRUE
Show code 
sum(multiplicity(airbnb0619_ppp) > 1)

[1] 6

From the result above, there are 6 duplicates in airbnb0619_ppp object.

Show code 
any(duplicated(airbnb0721_ppp))

[1] TRUE
Show code 
sum(multiplicity(airbnb0721_ppp) > 1)

[1] 224

From the result above, there are 224 duplicates in airbnb0721_ppp object.

Show code 
any(duplicated(hotels_ppp))

[1] TRUE
Show code 
sum(multiplicity(hotels_ppp) > 1)

[1] 10

From the result above, there are 10 duplicates in hotels_ppp object.

Show code 
any(duplicated(tourism_ppp))

[1] TRUE
Show code 
sum(multiplicity(tourism_ppp) > 1)

[1] 7

From the result above, there are 7 duplicates in tourism_ppp object.

Show code 
any(duplicated(mrtlrt_ppp))

[1] FALSE
Show code 
sum(multiplicity(mrtlrt_ppp) > 1)

[1] 0

From the result above, there are no duplicate in mrtlrt_ppp object.

4.3.4.2 Handling duplicates

We will use jittering to handle duplicates so that we will not lose useful points. Jittering adds a small perturbation to the duplicated points so that they do not occupy the exact same space.

The following code chunk uses rjitter function of spatstat to jitter the ppp object with duplicates.

Show code 
airbnb0619_ppp_jit <- rjitter(airbnb0619_ppp, 
                             retry=TRUE, 
                             nsim=1, 
                             drop=TRUE)

airbnb0721_ppp_jit <- rjitter(airbnb0721_ppp, 
                             retry=TRUE, 
                             nsim=1, 
                             drop=TRUE)

hotels_ppp_jit <- rjitter(hotels_ppp, 
                             retry=TRUE, 
                             nsim=1, 
                             drop=TRUE)

tourism_ppp_jit <- rjitter(tourism_ppp, 
                             retry=TRUE, 
                             nsim=1, 
                             drop=TRUE)

4.3.4.3 Check if duplicates still exist

Show code 
any(duplicated(airbnb0619_ppp_jit))

[1] FALSE
Show code 
sum(multiplicity(airbnb0619_ppp_jit) > 1)

[1] 0
Show code 
any(duplicated(airbnb0721_ppp_jit))

[1] FALSE
Show code 
sum(multiplicity(airbnb0721_ppp_jit) > 1)

[1] 0
Show code 
any(duplicated(hotels_ppp_jit))

[1] FALSE
Show code 
sum(multiplicity(hotels_ppp_jit) > 1)

[1] 0
Show code 
any(duplicated(tourism_ppp_jit))

[1] FALSE
Show code 
sum(multiplicity(tourism_ppp_jit) > 1)

[1] 0

We can confirm that there are no duplicated points after jittering.

4.3.5 Create owin objects

We will next create owin object to represent the Singapore boundary.

The following code chunk converts sg SpatialPolygon objects into owin object of spatstat using as function.

Show code 
sg_owin <- as(sg_sp, "owin")

The following code chunk plots sg_owin using plot function of base R package.

Show code 
plot(sg_owin)

4.3.6 Combining point events object and owin object

The following code chunk combines the point objects of airbnb0619, airbnb0721, hotels, tourism and mrtlrt with the owin object into one ppp object class.

Show code 
airbnb0619SG_ppp = airbnb0619_ppp_jit[sg_owin]
airbnb0721SG_ppp = airbnb0721_ppp_jit[sg_owin]
hotelsSG_ppp = hotels_ppp_jit[sg_owin]
tourismSG_ppp = tourism_ppp_jit[sg_owin]
mrtlrtSG_ppp = mrtlrt_ppp[sg_owin]

The following code chunk plots the ppp object class using plot function of base R package.

Show code 
plot(airbnb0619SG_ppp)

Show code 
plot(airbnb0721SG_ppp)

Show code 
plot(hotelsSG_ppp)

Show code 
plot(tourismSG_ppp)

Show code 
plot(mrtlrtSG_ppp)

5 Section A: Airbnb Distribution in 2019

5.1 Exploratory Spatial Data Analysis

5.1.1 Kernel density maps

5.1.1.1 Airbnb0619 listings

5.1.1.1.1 Compute kernel density estimation with automatic bandwidth selection method
  1. The following code chunk computes kernel density using density function of spatstat package.
  2. The code first takes in the ppp object variable.
  3. bw.diggle automatic bandwidth method to detect a tight cluster
  4. smoothing kernel is set to default gaussian method
Show code 
kde_airbnb0619SG_bw <- density(airbnb0619SG_ppp,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian")

The following code chunk plots the kde_airbnb0619SG_bw using plot function of base R

Show code 
plot(kde_airbnb0619SG_bw)

The density values of the output range is too small because the default unit of measurement of svy21 is in meter. As a result, the density values computed is in “number of points per square meter”.

5.1.1.1.2 Rescalling KDE values

The following code chunk rescale the KDE value of airbnb0619SG_ppp using rescale function of spatstat from meter to kilometer.

Show code 
airbnb0619SG_ppp.km <- rescale(airbnb0619SG_ppp, 1000, "km")
5.1.1.1.3 Different automatic bandwidth methods

The following code chunk calculate the bandwidth using different methods

Show code 
bw.CvL(airbnb0619SG_ppp.km)

   sigma 
5.987537
Show code 
bw.scott(airbnb0619SG_ppp.km)

 sigma.x  sigma.y 
1.074842 0.742618
Show code 
bw.ppl(airbnb0619SG_ppp.km)

    sigma 
0.2612563
Show code 
bw.diggle(airbnb0619SG_ppp.km)

     sigma 
0.04196627

From the result above, since bw.diggle has the lowest sigma, we will be using it for the other ppp object in order to detect close cluster.

5.1.1.1.4 KDE Map and Point Map
  1. The following code chunk computes kernel density using density function of spatstat package.
  2. The code first takes in the ppp object variable in km.
  3. bw.diggle automatic bandwidth method to detect a tight cluster
  4. smoothing kernel is set to default gaussian method
  5. plot function of base R package and par function of graphics package is used to plot kde map and ppp point map side by side
Show code 
kde_airbnb0619SG_bw.km <- density(airbnb0619SG_ppp.km,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian")


par(mfrow=c(1,2))
plot(kde_airbnb0619SG_bw.km)
plot(airbnb0619SG_ppp)

5.1.1.1.5 Convert KDE output into grid object

The following code chunk converts KDE output into grid object using as.SpatialGridDataFrame.im function of maptools, and plot the grid object using spplot function of raster package.

Show code 
gridded_kde_airbnb0619SG_bw <- as.SpatialGridDataFrame.im(kde_airbnb0619SG_bw.km)
spplot(gridded_kde_airbnb0619SG_bw)

5.1.1.1.6 Convert grid object into raster

The following code chunk converts grid object into raster using raster function of raster package

Show code 
kde_airbnb0619SG_bw_raster <- raster(gridded_kde_airbnb0619SG_bw)
kde_airbnb0619SG_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : v 
values     : -1.879102e-13, 1748.019  (min, max)

From the result above, the crs is NA

5.1.1.1.7 Assign project system

The following code chunk assign CRS to kde_airbnb0619SG_bw_raster

Show code 
projection(kde_airbnb0619SG_bw_raster) <- CRS("+init=EPSG:3414")
kde_airbnb0619SG_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +units=m +no_defs 
source     : memory
names      : v 
values     : -1.879102e-13, 1748.019  (min, max)

5.1.1.2 Hotels

5.1.1.2.1 Rescale KDE values

The following code chunk rescale the KDE value of hotelsSG_ppp using rescale function of spatstat from meter to kilometer.

Show code 
hotelsSG_ppp.km <- rescale(hotelsSG_ppp, 1000, "km")
5.1.1.2.2 KDE Map and Point Map
  1. The following code chunk computes kernel density using density function of spatstat package.
  2. The code first takes in the ppp object variable in km.
  3. bw.diggle automatic bandwidth method to detect a tight cluster
  4. smoothing kernel is set to default gaussian method
  5. plot function of base R package and par function of graphics package is used to plot kde map and ppp point map side by side
Show code 
kde_hotelsSG_bw.km <- density(hotelsSG_ppp.km,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian")

par(mfrow=c(1,2))
plot(kde_hotelsSG_bw.km, main = "KDE Map")
plot(hotelsSG_ppp, main="Point Map")

5.1.1.2.3 Convert KDE output into grid object

The following code chunk converts KDE output into grid object using as.SpatialGridDataFrame.im function of maptools, and plot the grid object using spplot function of raster package.

Show code 
gridded_kde_hotelsSG_bw <- as.SpatialGridDataFrame.im(kde_hotelsSG_bw.km)
spplot(gridded_kde_hotelsSG_bw)

5.1.1.2.4 Convert grid object into raster and assign project system

The following code chunk converts grid object into raster using raster function of raster package

Show code 
kde_hotelsSG_bw_raster <- raster(gridded_kde_hotelsSG_bw)
projection(kde_hotelsSG_bw_raster) <- CRS("+init=EPSG:3414")
kde_hotelsSG_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +units=m +no_defs 
source     : memory
names      : v 
values     : -2.137411e-14, 225.6956  (min, max)

5.1.1.3 MRTLRT

5.1.1.3.1 Rescale KDE values

The following code chunk rescale the KDE value of mrtlrtSG_ppp using rescale function of spatstat from meter to kilometer.

Show code 
mrtlrtSG_ppp.km <- rescale(mrtlrtSG_ppp, 1000, "km")
5.1.1.3.2 KDE Map and Point Map
  1. The following code chunk computes kernel density using density function of spatstat package.
  2. The code first takes in the ppp object variable in km.
  3. bw.diggle automatic bandwidth method to detect a tight cluster
  4. smoothing kernel is set to default gaussian method
  5. plot function of base R package and par function of graphics package is used to plot kde map and ppp point map side by side
Show code 
kde_mrtlrtSG_bw.km <- density(mrtlrtSG_ppp.km,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian")

par(mfrow=c(1,2))
plot(kde_mrtlrtSG_bw.km, main = "KDE Map")
plot(mrtlrtSG_ppp, main="Point Map")

5.1.1.3.3 Convert KDE output into grid object

The following code chunk converts KDE output into grid object using as.SpatialGridDataFrame.im function of maptools, and plot the grid object using spplot function of raster package.

Show code 
gridded_kde_mrtlrtSG_bw <- as.SpatialGridDataFrame.im(kde_mrtlrtSG_bw.km)
spplot(gridded_kde_mrtlrtSG_bw)

5.1.1.3.4 Convert grid object into raster and assign project system

The following code chunk converts grid object into raster using raster function of raster package

Show code 
kde_mrtlrtSG_bw_raster <- raster(gridded_kde_mrtlrtSG_bw)
projection(kde_mrtlrtSG_bw_raster) <- CRS("+init=EPSG:3414")
kde_mrtlrtSG_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +units=m +no_defs 
source     : memory
names      : v 
values     : -1.443225e-15, 3.580091  (min, max)

5.1.1.4 Tourist Attractions

5.1.1.4.1 Rescale KDE values

The following code chunk rescale the KDE value of mrtlrtSG_ppp using rescale function of spatstat from meter to kilometer.

Show code 
tourismSG_ppp.km <- rescale(tourismSG_ppp, 1000, "km")
5.1.1.4.2 KDE Map and Point Map
  1. The following code chunk computes kernel density using density function of spatstat package.
  2. The code first takes in the ppp object variable in km.
  3. bw.diggle automatic bandwidth method to detect a tight cluster
  4. smoothing kernel is set to default gaussian method
  5. plot function of base R package and par function of graphics package is used to plot kde map and ppp point map side by side
Show code 
kde_tourismSG_bw.km <- density(tourismSG_ppp.km,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian")

par(mfrow=c(1,2))
plot(kde_tourismSG_bw.km, main = "KDE Map")
plot(tourismSG_ppp, main="Point Map")

5.1.1.4.3 Convert KDE output into grid object

The following code chunk converts KDE output into grid object using as.SpatialGridDataFrame.im function of maptools, and plot the grid object using spplot function of raster package.

Show code 
gridded_kde_tourismSG_bw <- as.SpatialGridDataFrame.im(kde_tourismSG_bw.km)
spplot(gridded_kde_tourismSG_bw)

5.1.1.4.4 Convert grid object into raster and assign project system

The following code chunk converts grid object into raster using raster function of raster package

Show code 
kde_tourismSG_bw_raster <- raster(gridded_kde_tourismSG_bw)
projection(kde_tourismSG_bw_raster) <- CRS("+init=EPSG:3414")
kde_tourismSG_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +units=m +no_defs 
source     : memory
names      : v 
values     : -2.090612e-15, 21.6271  (min, max)

5.1.1.5 Comparison between Airbnb0619, Hotels, MRTLRT and Tourist Attractions

5.1.1.5.1 KDE Map Comparison

The following code chunk plots all the KDE map of Airbnb0619, Hotels, MRTLRT and Tourist Attractions side by side using par functions of graphics package

Show code 
par(mfrow=c(2,2))
plot(kde_airbnb0619SG_bw.km, main = "Airbnb 2019")
plot(kde_hotelsSG_bw.km, main = "Hotel")
plot(kde_mrtlrtSG_bw.km, main = "Mrt")
plot(kde_tourismSG_bw.km, main = "Tourist Attraction")

From the above, we can see that the higher density (in red and yellow) for airbnb, hotel, mrt and tourish attraction are concentrated around the south east area below central region.

5.1.1.5.2 Central Region

We will look into the central region

5.1.1.5.2.1 Exploration with OpenStreetMap

The following code chunk first extract airbnb in the central region from airbnb0619_sf region.

tmap_mode is set to “view” for interaction mode and tm_basemap is set to “OpenStreetMap”

tm_shape of centralairbnb0619, hotels_sf, mrtlrt_sf and tourism_sf were plotted using tm_shape and tm_dots of tmap packages.

colour of dots using tm_dots and the following are the color representation:

Show code 
centralairbnb0619 <- airbnb0619_sf[airbnb0619_sf$neighbourhood_group == "Central Region",]

tmap_mode("view")

tm_shape(centralairbnb0619) + 
  tm_dots(col="black", size = 0.02)+
tm_shape(hotels_sf) + 
  tm_dots(col="green", size = 0.02)+
tm_shape(mrtlrt_sf) + 
  tm_dots(col="blue", size = 0.02)+
tm_shape(tourism_sf) + 
  tm_dots(col="red", size = 0.02)

From the map above, we can see that there are many Airbnbs located in Kallang, River Valley, Novena an Rochor.

Show code 
tmap_mode("plot")